Center for Monitoring and Research of the Quality of Fuels, Biofuels, Crude Oil, and Derivatives (Cempeqc), Institute of Chemistry, São Paulo State University (UNESP), Prof. Francisco Degni 55, Araraquara, SP, Zip Code 14800-060, Brazil.
Environ Monit Assess. 2018 Jan 9;190(2):72. doi: 10.1007/s10661-017-6454-9.
Environmental contamination caused by leakage of fuels and lubricant oils at gas stations is of great concern due to the presence of carcinogenic compounds in the composition of gasoline, diesel, and mineral lubricant oils. Chromatographic methods or non-selective infrared methods are usually used to assess soil contamination, which makes environmental monitoring costly or not appropriate. In this perspective, the present work proposes a methodology to identify the type of contaminant (gasoline, diesel, or lubricant oil) and, subsequently, to quantify the contaminant concentration using attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy and multivariate methods. Firstly, gasoline, diesel, and lubricating oil samples were acquired from gas stations and analyzed by gas chromatography to determine the total petroleum hydrocarbon (TPH) fractions (gasoline range organics, diesel range organics, and oil range organics). Then, solutions of these contaminants in hexane were prepared in the concentration range of about 5-10,000 mg kg. The infrared spectra of the solutions were obtained and used for the development of the pattern recognition model and the calibration models. The partial least square discriminant analysis (PLS-DA) model could correctly classify 100% of the samples of each type of contaminant and presented selectivity equal to 1.00, which provides a suitable method for the identification of the source of contamination. The PLS regression models were developed using multivariate filters, such as orthogonal signal correction (OSC) and general least square weighting (GLSW), and selection variable by genetic algorithm (GA). The validation of the models resulted in correlation coefficients above 0.96 and root-mean-square error of prediction values below the maximum permissible contamination limit (1000 mg kg). The methodology was validated through the addition of fuels and lubricating oil in soil samples and quantification of the TPH fractions through the developed models after the extraction of the analytes by the EPA 3550 method adapted by the authors. The recovery percentage of the analytes was within the acceptance limits of ASTM D7678 (70-130%), except for one sample (69% of recovery). Therefore, the methodology proposed here provides faster and less costly analyses than the chromatographic methods and it is adequate for the environmental monitoring of soil contamination by gas stations.
加油站燃料和润滑油泄漏导致的环境污染问题引起了极大关注，因为汽油、柴油和矿物润滑油的成分中存在致癌化合物。通常使用色谱方法或非选择性红外方法来评估土壤污染，这使得环境监测成本高昂或不适用。在这种情况下，本工作提出了一种使用衰减全反射傅里叶变换红外（ATR-FTIR）光谱和多元方法来识别污染物类型（汽油、柴油或润滑油）并随后定量污染物浓度的方法。首先，从加油站采集汽油、柴油和润滑油样品，并通过气相色谱法分析以确定总石油烃（TPH）分数（汽油范围有机物、柴油范围有机物和油范围有机物）。然后，在约 5-10000mgkg 的浓度范围内制备这些污染物在己烷中的溶液。获得溶液的红外光谱，并将其用于开发模式识别模型和校准模型。偏最小二乘判别分析（PLS-DA）模型可以正确分类每种类型污染物的 100%的样品，且具有等于 1.00 的选择性，这为识别污染源提供了一种合适的方法。使用多元过滤器（例如正交信号校正（OSC）和广义最小二乘加权（GLSW））和遗传算法（GA）选择变量开发了 PLS 回归模型。通过在土壤样品中添加燃料和润滑油，并通过作者改编的 EPA 3550 方法提取分析物后，使用开发的模型定量 TPH 分数来验证模型，得到的模型相关性系数均高于 0.96，预测值的均方根误差低于最大允许污染限值（1000mgkg）。通过在土壤样品中添加燃料和润滑油，并使用作者改编的 EPA 3550 方法提取分析物后，使用开发的模型定量 TPH 分数来验证该方法，结果表明，除了一个样本（回收率为 69%）外，分析物的回收率均在 ASTM D7678 的可接受范围内（70-130%）。因此，与色谱方法相比，本方法提供了更快、成本更低的分析，适用于加油站土壤污染的环境监测。
